Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations3678
Missing cells6708
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory637.1 B

Variable types

Categorical10
Text4
Numeric9

Alerts

bathroom is highly overall correlated with bedRoom and 4 other fieldsHigh correlation
bedRoom is highly overall correlated with bathroom and 4 other fieldsHigh correlation
built_up_area is highly overall correlated with carpet_area and 2 other fieldsHigh correlation
carpet_area is highly overall correlated with bathroom and 4 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
price is highly overall correlated with bathroom and 5 other fieldsHigh correlation
price_per_sq_ft is highly overall correlated with priceHigh correlation
property_type is highly overall correlated with bedRoom and 2 other fieldsHigh correlation
servant room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
super_built_up_area is highly overall correlated with bathroom and 6 other fieldsHigh correlation
store room is highly imbalanced (55.7%) Imbalance
facing has 1046 (28.4%) missing values Missing
super_built_up_area has 1803 (49.0%) missing values Missing
built_up_area has 1988 (54.1%) missing values Missing
carpet_area has 1806 (49.1%) missing values Missing
built_up_area is highly skewed (γ1 = 40.77881958) Skewed
carpet_area is highly skewed (γ1 = 24.33323909) Skewed
floorNum has 129 (3.5%) zeros Zeros
luxury_score has 463 (12.6%) zeros Zeros

Reproduction

Analysis started2025-07-04 14:25:37.401051
Analysis finished2025-07-04 14:25:58.290409
Duration20.89 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

property_type
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size219.9 KiB
flat
2818 
house
859 

Length

Max length5
Median length4
Mean length4.2336144
Min length4

Characters and Unicode

Total characters15567
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowhouse
4th rowhouse
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2818
76.6%
house 859
 
23.4%
(Missing) 1
 
< 0.1%

Length

2025-07-04T19:55:58.425897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T19:55:58.553436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
flat 2818
76.6%
house 859
 
23.4%

Most occurring characters

ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15567
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 15567
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15567
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%
Distinct676
Distinct (%)18.4%
Missing2
Missing (%)0.1%
Memory size265.3 KiB
2025-07-04T19:55:59.161650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.869695
Min length1

Characters and Unicode

Total characters62013
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique308 ?
Unique (%)8.4%

Sample

1st rowdlf the belaire
2nd rowimperia the esfera
3rd rowindependent
4th rowu block dlf phase 3 road no 21
5th rowmrg skyline
ValueCountFrequency (%)
independent 491
 
5.1%
the 350
 
3.6%
dlf 220
 
2.3%
park 209
 
2.2%
city 166
 
1.7%
emaar 155
 
1.6%
global 153
 
1.6%
m3m 152
 
1.6%
signature 150
 
1.6%
heights 134
 
1.4%
Other values (783) 7497
77.5%
2025-07-04T19:55:59.985989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6710
 
10.8%
6003
 
9.7%
a 5861
 
9.5%
r 4171
 
6.7%
n 4163
 
6.7%
i 3830
 
6.2%
t 3719
 
6.0%
s 3472
 
5.6%
l 2943
 
4.7%
o 2755
 
4.4%
Other values (31) 18386
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55465
89.4%
Space Separator 6003
 
9.7%
Decimal Number 527
 
0.8%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6710
12.1%
a 5861
 
10.6%
r 4171
 
7.5%
n 4163
 
7.5%
i 3830
 
6.9%
t 3719
 
6.7%
s 3472
 
6.3%
l 2943
 
5.3%
o 2755
 
5.0%
d 2488
 
4.5%
Other values (16) 15353
27.7%
Decimal Number
ValueCountFrequency (%)
3 207
39.3%
2 82
 
15.6%
1 75
 
14.2%
6 56
 
10.6%
8 32
 
6.1%
4 19
 
3.6%
5 17
 
3.2%
9 13
 
2.5%
0 13
 
2.5%
7 13
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 7
70.0%
/ 2
 
20.0%
. 1
 
10.0%
Space Separator
ValueCountFrequency (%)
6003
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55465
89.4%
Common 6548
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6710
12.1%
a 5861
 
10.6%
r 4171
 
7.5%
n 4163
 
7.5%
i 3830
 
6.9%
t 3719
 
6.7%
s 3472
 
6.3%
l 2943
 
5.3%
o 2755
 
5.0%
d 2488
 
4.5%
Other values (16) 15353
27.7%
Common
ValueCountFrequency (%)
6003
91.7%
3 207
 
3.2%
2 82
 
1.3%
1 75
 
1.1%
6 56
 
0.9%
8 32
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
9 13
 
0.2%
0 13
 
0.2%
Other values (5) 31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6710
 
10.8%
6003
 
9.7%
a 5861
 
9.5%
r 4171
 
6.7%
n 4163
 
6.7%
i 3830
 
6.2%
t 3719
 
6.0%
s 3472
 
5.6%
l 2943
 
4.7%
o 2755
 
4.4%
Other values (31) 18386
29.6%

sector
Text

Distinct115
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size238.2 KiB
2025-07-04T19:56:00.517880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.3181077
Min length3

Characters and Unicode

Total characters34272
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 54
2nd rowsector 37c
3rd rowsector 3
4th rowsector 24
5th rowsector 106
ValueCountFrequency (%)
sector 3449
46.7%
road 178
 
2.4%
sohna 166
 
2.2%
85 108
 
1.5%
102 107
 
1.4%
92 100
 
1.4%
69 93
 
1.3%
90 89
 
1.2%
81 87
 
1.2%
65 87
 
1.2%
Other values (107) 2921
39.6%
2025-07-04T19:56:01.178986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3804
11.1%
3707
10.8%
s 3694
10.8%
r 3694
10.8%
e 3548
10.4%
c 3500
10.2%
t 3460
10.1%
1 1075
 
3.1%
0 802
 
2.3%
8 778
 
2.3%
Other values (21) 6210
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23308
68.0%
Decimal Number 7257
 
21.2%
Space Separator 3707
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3804
16.3%
s 3694
15.8%
r 3694
15.8%
e 3548
15.2%
c 3500
15.0%
t 3460
14.8%
a 699
 
3.0%
d 249
 
1.1%
n 230
 
1.0%
h 203
 
0.9%
Other values (10) 227
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 1075
14.8%
0 802
11.1%
8 778
10.7%
9 761
10.5%
6 739
10.2%
7 682
9.4%
2 681
9.4%
3 665
9.2%
5 592
8.2%
4 482
6.6%
Space Separator
ValueCountFrequency (%)
3707
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23308
68.0%
Common 10964
32.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3804
16.3%
s 3694
15.8%
r 3694
15.8%
e 3548
15.2%
c 3500
15.0%
t 3460
14.8%
a 699
 
3.0%
d 249
 
1.1%
n 230
 
1.0%
h 203
 
0.9%
Other values (10) 227
 
1.0%
Common
ValueCountFrequency (%)
3707
33.8%
1 1075
 
9.8%
0 802
 
7.3%
8 778
 
7.1%
9 761
 
6.9%
6 739
 
6.7%
7 682
 
6.2%
2 681
 
6.2%
3 665
 
6.1%
5 592
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3804
11.1%
3707
10.8%
s 3694
10.8%
r 3694
10.8%
e 3548
10.4%
c 3500
10.2%
t 3460
10.1%
1 1075
 
3.1%
0 802
 
2.3%
8 778
 
2.3%
Other values (21) 6210
18.1%

price
Real number (ℝ)

High correlation 

Distinct473
Distinct (%)12.9%
Missing18
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.5336639
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-04T19:56:01.483071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9806235
Coefficient of variation (CV)1.1764084
Kurtosis14.933373
Mean2.5336639
Median Absolute Deviation (MAD)0.72
Skewness3.2791705
Sum9273.21
Variance8.8841164
MonotonicityNot monotonic
2025-07-04T19:56:01.797190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.2 64
 
1.7%
1.5 64
 
1.7%
0.9 63
 
1.7%
1.1 62
 
1.7%
1.4 60
 
1.6%
1.3 57
 
1.5%
0.95 52
 
1.4%
2 52
 
1.4%
1.6 48
 
1.3%
Other values (463) 3058
83.1%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 8
0.2%
0.23 1
 
< 0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sq_ft
Real number (ℝ)

High correlation 

Distinct2651
Distinct (%)72.4%
Missing18
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13892.668
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-04T19:56:02.029825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4715.95
Q16817.25
median9020
Q313880.5
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7063.25

Descriptive statistics

Standard deviation23210.067
Coefficient of variation (CV)1.6706702
Kurtosis186.92801
Mean13892.668
Median Absolute Deviation (MAD)2794
Skewness11.43719
Sum50847166
Variance5.3870722 × 108
MonotonicityNot monotonic
2025-07-04T19:56:02.194578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
6666 13
 
0.4%
11111 13
 
0.4%
22222 13
 
0.4%
7500 12
 
0.3%
8333 12
 
0.3%
33333 11
 
0.3%
Other values (2641) 3509
95.4%
(Missing) 18
 
0.5%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Text

Distinct2674
Distinct (%)72.7%
Missing2
Missing (%)0.1%
Memory size268.6 KiB
2025-07-04T19:56:02.658026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length25
Median length24
Mean length17.792982
Min length5

Characters and Unicode

Total characters65407
Distinct characters31
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2287 ?
Unique (%)62.2%

Sample

1st row4072.158651301055
2nd row1665.7899351218657
3rd row(93 sq.m.) Built-up Area
4th row(50 sq.m.) Plot Area
5th row1359.020310633214
ValueCountFrequency (%)
sq.m 859
 
13.7%
area 859
 
13.7%
plot 681
 
10.9%
built-up 137
 
2.2%
301 46
 
0.7%
carpet 41
 
0.7%
251 33
 
0.5%
2000.0 31
 
0.5%
84 30
 
0.5%
167 26
 
0.4%
Other values (2584) 3510
56.1%
2025-07-04T19:56:03.507938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6599
 
10.1%
1 6303
 
9.6%
2 5098
 
7.8%
5 4656
 
7.1%
. 4535
 
6.9%
4 4310
 
6.6%
3 4149
 
6.3%
6 4086
 
6.2%
8 3787
 
5.8%
9 3768
 
5.8%
Other values (21) 18116
27.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46498
71.1%
Lowercase Letter 8224
 
12.6%
Other Punctuation 4535
 
6.9%
Space Separator 2577
 
3.9%
Uppercase Letter 1718
 
2.6%
Open Punctuation 859
 
1.3%
Close Punctuation 859
 
1.3%
Dash Punctuation 137
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 900
10.9%
a 900
10.9%
e 900
10.9%
t 859
10.4%
m 859
10.4%
q 859
10.4%
s 859
10.4%
l 818
9.9%
o 681
8.3%
u 274
 
3.3%
Other values (2) 315
 
3.8%
Decimal Number
ValueCountFrequency (%)
0 6599
14.2%
1 6303
13.6%
2 5098
11.0%
5 4656
10.0%
4 4310
9.3%
3 4149
8.9%
6 4086
8.8%
8 3787
8.1%
9 3768
8.1%
7 3742
8.0%
Uppercase Letter
ValueCountFrequency (%)
A 859
50.0%
P 681
39.6%
B 137
 
8.0%
C 41
 
2.4%
Other Punctuation
ValueCountFrequency (%)
. 4535
100.0%
Space Separator
ValueCountFrequency (%)
2577
100.0%
Open Punctuation
ValueCountFrequency (%)
( 859
100.0%
Close Punctuation
ValueCountFrequency (%)
) 859
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 137
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 55465
84.8%
Latin 9942
 
15.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 900
9.1%
a 900
9.1%
e 900
9.1%
A 859
8.6%
t 859
8.6%
m 859
8.6%
q 859
8.6%
s 859
8.6%
l 818
8.2%
o 681
6.8%
Other values (6) 1448
14.6%
Common
ValueCountFrequency (%)
0 6599
11.9%
1 6303
11.4%
2 5098
9.2%
5 4656
8.4%
. 4535
8.2%
4 4310
7.8%
3 4149
7.5%
6 4086
7.4%
8 3787
6.8%
9 3768
6.8%
Other values (5) 8174
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65407
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6599
 
10.1%
1 6303
 
9.6%
2 5098
 
7.8%
5 4656
 
7.1%
. 4535
 
6.9%
4 4310
 
6.6%
3 4149
 
6.3%
6 4086
 
6.2%
8 3787
 
5.8%
9 3768
 
5.8%
Other values (21) 18116
27.7%
Distinct2355
Distinct (%)64.0%
Missing1
Missing (%)< 0.1%
Memory size399.5 KiB
2025-07-04T19:56:04.318343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length124
Median length119
Mean length54.236062
Min length12

Characters and Unicode

Total characters199426
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1849 ?
Unique (%)50.3%

Sample

1st rowSuper Built up area 4072(378.3 sq.m.)Built Up area: 3000 sq.ft. (278.71 sq.m.)Carpet area: 2800 sq.ft. (260.13 sq.m.)
2nd rowBuilt Up area: 1578 (146.6 sq.m.)
3rd rowBuilt Up area: 1000 (92.9 sq.m.)
4th rowPlot area 60(50.17 sq.m.)
5th rowBuilt Up area: 1359 (126.26 sq.m.)Carpet area: 952 sq.ft. (88.44 sq.m.)
ValueCountFrequency (%)
area 5573
18.5%
sq.m 3655
12.1%
up 3020
 
10.0%
built 2316
 
7.7%
super 1875
 
6.2%
sq.ft 1751
 
5.8%
sq.m.)carpet 1185
 
3.9%
sq.m.)built 702
 
2.3%
carpet 683
 
2.3%
plot 681
 
2.3%
Other values (2846) 8700
28.9%
2025-07-04T19:56:05.182467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26464
 
13.3%
. 20389
 
10.2%
a 13154
 
6.6%
r 9456
 
4.7%
e 9320
 
4.7%
1 9205
 
4.6%
s 7567
 
3.8%
q 7431
 
3.7%
t 7324
 
3.7%
u 6770
 
3.4%
Other values (25) 82346
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82758
41.5%
Decimal Number 47135
23.6%
Space Separator 26464
 
13.3%
Other Punctuation 23406
 
11.7%
Uppercase Letter 8593
 
4.3%
Close Punctuation 5535
 
2.8%
Open Punctuation 5535
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13154
15.9%
r 9456
11.4%
e 9320
11.3%
s 7567
9.1%
q 7431
9.0%
t 7324
8.8%
u 6770
8.2%
p 6767
8.2%
m 5544
6.7%
l 3701
 
4.5%
Other values (5) 5724
6.9%
Decimal Number
ValueCountFrequency (%)
1 9205
19.5%
0 6628
14.1%
2 5688
12.1%
5 4714
10.0%
3 3960
8.4%
4 3711
7.9%
6 3674
 
7.8%
7 3254
 
6.9%
8 3157
 
6.7%
9 3144
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3020
35.1%
S 1875
21.8%
C 1872
21.8%
U 1145
 
13.3%
P 681
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 20389
87.1%
: 3017
 
12.9%
Space Separator
ValueCountFrequency (%)
26464
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5535
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5535
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108075
54.2%
Latin 91351
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13154
14.4%
r 9456
10.4%
e 9320
10.2%
s 7567
8.3%
q 7431
8.1%
t 7324
8.0%
u 6770
7.4%
p 6767
7.4%
m 5544
 
6.1%
l 3701
 
4.1%
Other values (10) 14317
15.7%
Common
ValueCountFrequency (%)
26464
24.5%
. 20389
18.9%
1 9205
 
8.5%
0 6628
 
6.1%
2 5688
 
5.3%
) 5535
 
5.1%
( 5535
 
5.1%
5 4714
 
4.4%
3 3960
 
3.7%
4 3711
 
3.4%
Other values (5) 16246
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26464
 
13.3%
. 20389
 
10.2%
a 13154
 
6.6%
r 9456
 
4.7%
e 9320
 
4.7%
1 9205
 
4.6%
s 7567
 
3.8%
q 7431
 
3.7%
t 7324
 
3.7%
u 6770
 
3.4%
Other values (25) 82346
41.3%

bedRoom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.3600761
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-04T19:56:05.448845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8976289
Coefficient of variation (CV)0.56475771
Kurtosis18.212873
Mean3.3600761
Median Absolute Deviation (MAD)1
Skewness3.4851418
Sum12355
Variance3.6009954
MonotonicityNot monotonic
2025-07-04T19:56:05.730307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1496
40.7%
2 942
25.6%
4 660
17.9%
5 210
 
5.7%
1 124
 
3.4%
6 74
 
2.0%
9 41
 
1.1%
8 30
 
0.8%
7 28
 
0.8%
12 28
 
0.8%
Other values (9) 44
 
1.2%
ValueCountFrequency (%)
1 124
 
3.4%
2 942
25.6%
3 1496
40.7%
4 660
17.9%
5 210
 
5.7%
6 74
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.4245309
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-04T19:56:05.980325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9480681
Coefficient of variation (CV)0.56885693
Kurtosis17.542297
Mean3.4245309
Median Absolute Deviation (MAD)1
Skewness3.2488298
Sum12592
Variance3.7949693
MonotonicityNot monotonic
2025-07-04T19:56:06.196615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1077
29.3%
2 1047
28.5%
4 820
22.3%
5 294
 
8.0%
1 156
 
4.2%
6 117
 
3.2%
9 41
 
1.1%
7 40
 
1.1%
8 25
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
ValueCountFrequency (%)
1 156
 
4.2%
2 1047
28.5%
3 1077
29.3%
4 820
22.3%
5 294
 
8.0%
6 117
 
3.2%
7 40
 
1.1%
8 25
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size209.5 KiB
3+
1172 
3
1074 
2
884 
1
365 
0
182 

Length

Max length2
Median length1
Mean length1.3187381
Min length1

Characters and Unicode

Total characters4849
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3+
2nd row0
3rd row0
4th row3+
5th row2

Common Values

ValueCountFrequency (%)
3+ 1172
31.9%
3 1074
29.2%
2 884
24.0%
1 365
 
9.9%
0 182
 
4.9%
(Missing) 1
 
< 0.1%

Length

2025-07-04T19:56:06.359850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T19:56:06.569753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 2246
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 182
 
4.9%

Most occurring characters

ValueCountFrequency (%)
3 2246
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
75.8%
Math Symbol 1172
 
24.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2246
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 182
 
4.9%
Math Symbol
ValueCountFrequency (%)
+ 1172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4849
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2246
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4849
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2246
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

floorNum
Real number (ℝ)

Zeros 

Distinct43
Distinct (%)1.2%
Missing20
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.7982504
Minimum0
Maximum51
Zeros129
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-04T19:56:06.807004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0124542
Coefficient of variation (CV)0.884412
Kurtosis4.5153928
Mean6.7982504
Median Absolute Deviation (MAD)3
Skewness1.6936988
Sum24868
Variance36.149606
MonotonicityNot monotonic
2025-07-04T19:56:07.181659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 498
13.5%
2 493
13.4%
1 351
 
9.5%
4 316
 
8.6%
8 195
 
5.3%
6 183
 
5.0%
10 179
 
4.9%
7 176
 
4.8%
5 169
 
4.6%
9 161
 
4.4%
Other values (33) 937
25.5%
ValueCountFrequency (%)
0 129
 
3.5%
1 351
9.5%
2 493
13.4%
3 498
13.5%
4 316
8.6%
5 169
 
4.6%
6 183
 
5.0%
7 176
 
4.8%
8 195
 
5.3%
9 161
 
4.4%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

High correlation  Missing 

Distinct8
Distinct (%)0.3%
Missing1046
Missing (%)28.4%
Memory size229.5 KiB
East
623 
North-East
623 
North
387 
West
249 
South
231 
Other values (3)
519 

Length

Max length10
Median length5
Mean length6.8381459
Min length4

Characters and Unicode

Total characters17998
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth
2nd rowEast
3rd rowEast
4th rowNorth-East
5th rowNorth

Common Values

ValueCountFrequency (%)
East 623
16.9%
North-East 623
16.9%
North 387
 
10.5%
West 249
 
6.8%
South 231
 
6.3%
North-West 193
 
5.2%
South-East 173
 
4.7%
South-West 153
 
4.2%
(Missing) 1046
28.4%

Length

2025-07-04T19:56:07.453128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T19:56:07.618495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
east 623
23.7%
north-east 623
23.7%
north 387
14.7%
west 249
 
9.5%
south 231
 
8.8%
north-west 193
 
7.3%
south-east 173
 
6.6%
south-west 153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13082
72.7%
Uppercase Letter 3774
 
21.0%
Dash Punctuation 1142
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3774
28.8%
s 2014
15.4%
o 1760
13.5%
h 1760
13.5%
a 1419
 
10.8%
r 1203
 
9.2%
e 595
 
4.5%
u 557
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
E 1419
37.6%
N 1203
31.9%
W 595
15.8%
S 557
 
14.8%
Dash Punctuation
ValueCountFrequency (%)
- 1142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16856
93.7%
Common 1142
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3774
22.4%
s 2014
11.9%
o 1760
10.4%
h 1760
10.4%
E 1419
 
8.4%
a 1419
 
8.4%
N 1203
 
7.1%
r 1203
 
7.1%
W 595
 
3.5%
e 595
 
3.5%
Other values (2) 1114
 
6.6%
Common
ValueCountFrequency (%)
- 1142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size252.8 KiB
Relatively New
1646 
New Property
593 
Moderately Old
563 
Undefined
307 
Old Property
303 

Length

Max length18
Median length14
Mean length13.38472
Min length9

Characters and Unicode

Total characters49229
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerately Old
2nd rowUnder Construction
3rd rowUndefined
4th rowOld Property
5th rowUndefined

Common Values

ValueCountFrequency (%)
Relatively New 1646
44.8%
New Property 593
 
16.1%
Moderately Old 563
 
15.3%
Undefined 307
 
8.3%
Old Property 303
 
8.2%
Under Construction 266
 
7.2%

Length

2025-07-04T19:56:07.784042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T19:56:07.963566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
new 2239
31.8%
relatively 1646
23.4%
property 896
12.7%
old 866
 
12.3%
moderately 563
 
8.0%
undefined 307
 
4.4%
under 266
 
3.8%
construction 266
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e 8433
17.1%
l 4721
 
9.6%
t 3637
 
7.4%
3371
 
6.8%
y 3105
 
6.3%
r 2887
 
5.9%
d 2309
 
4.7%
N 2239
 
4.5%
w 2239
 
4.5%
i 2219
 
4.5%
Other values (15) 14069
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38809
78.8%
Uppercase Letter 7049
 
14.3%
Space Separator 3371
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8433
21.7%
l 4721
12.2%
t 3637
9.4%
y 3105
 
8.0%
r 2887
 
7.4%
d 2309
 
5.9%
w 2239
 
5.8%
i 2219
 
5.7%
a 2209
 
5.7%
o 1991
 
5.1%
Other values (7) 5059
13.0%
Uppercase Letter
ValueCountFrequency (%)
N 2239
31.8%
R 1646
23.4%
P 896
12.7%
O 866
 
12.3%
U 573
 
8.1%
M 563
 
8.0%
C 266
 
3.8%
Space Separator
ValueCountFrequency (%)
3371
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45858
93.2%
Common 3371
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8433
18.4%
l 4721
 
10.3%
t 3637
 
7.9%
y 3105
 
6.8%
r 2887
 
6.3%
d 2309
 
5.0%
N 2239
 
4.9%
w 2239
 
4.9%
i 2219
 
4.8%
a 2209
 
4.8%
Other values (14) 11860
25.9%
Common
ValueCountFrequency (%)
3371
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8433
17.1%
l 4721
 
9.6%
t 3637
 
7.4%
3371
 
6.8%
y 3105
 
6.3%
r 2887
 
5.9%
d 2309
 
4.7%
N 2239
 
4.5%
w 2239
 
4.5%
i 2219
 
4.5%
Other values (15) 14069
28.6%

super_built_up_area
Real number (ℝ)

High correlation  Missing 

Distinct593
Distinct (%)31.6%
Missing1803
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1925.2376
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-04T19:56:08.240054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11479.5
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)735.5

Descriptive statistics

Standard deviation764.17218
Coefficient of variation (CV)0.39692356
Kurtosis10.349191
Mean1925.2376
Median Absolute Deviation (MAD)372
Skewness1.8364563
Sum3609820.5
Variance583959.12
MonotonicityNot monotonic
2025-07-04T19:56:08.924266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 37
 
1.0%
1950 37
 
1.0%
1578 25
 
0.7%
2000 25
 
0.7%
2150 22
 
0.6%
1640 22
 
0.6%
1900 19
 
0.5%
2408 19
 
0.5%
1930 18
 
0.5%
1350 17
 
0.5%
Other values (583) 1634
44.4%
(Missing) 1803
49.0%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct624
Distinct (%)36.9%
Missing1988
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean1841.9314
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-04T19:56:09.205007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile129.681
Q1360
median1256.5
Q31900
95-th percentile3936.5
Maximum737147
Range737145
Interquartile range (IQR)1540

Descriptive statistics

Standard deviation17945.374
Coefficient of variation (CV)9.7426944
Kurtosis1671.8347
Mean1841.9314
Median Absolute Deviation (MAD)754.5
Skewness40.77882
Sum3112864
Variance3.2203646 × 108
MonotonicityNot monotonic
2025-07-04T19:56:09.488466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360 45
 
1.2%
1900 34
 
0.9%
300 34
 
0.9%
1600 26
 
0.7%
1300 24
 
0.7%
2000 24
 
0.7%
1700 23
 
0.6%
200 22
 
0.6%
1800 22
 
0.6%
900 21
 
0.6%
Other values (614) 1415
38.5%
(Missing) 1988
54.1%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
40 4
0.1%
50 6
0.2%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
9500 1
 
< 0.1%
9000 4
0.1%
8286 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%
7450 1
 
< 0.1%
7331 2
 
0.1%
7000 9
0.2%
6500 1
 
< 0.1%

carpet_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct733
Distinct (%)39.2%
Missing1806
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean2529.1795
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-04T19:56:09.812923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1843
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)947

Descriptive statistics

Standard deviation22799.836
Coefficient of variation (CV)9.0147166
Kurtosis604.53764
Mean2529.1795
Median Absolute Deviation (MAD)472.5
Skewness24.333239
Sum4734624
Variance5.1983254 × 108
MonotonicityNot monotonic
2025-07-04T19:56:10.142049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1600 35
 
1.0%
1800 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1450 22
 
0.6%
1000 22
 
0.6%
Other values (723) 1578
42.9%
(Missing) 1806
49.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
2973 
1
705 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Length

2025-07-04T19:56:10.496309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T19:56:10.715372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring characters

ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2973
80.8%
1 705
 
19.2%

servant room
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
2350 
1
1328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Length

2025-07-04T19:56:10.991665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T19:56:11.206006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring characters

ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

store room
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
3340 
1
338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Length

2025-07-04T19:56:11.386659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T19:56:11.651049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
3022 
1
656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Length

2025-07-04T19:56:11.975822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T19:56:12.181978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3022
82.2%
1 656
 
17.8%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
3273 
1
405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Length

2025-07-04T19:56:12.479903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T19:56:12.678864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3273
89.0%
1 405
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size208.3 KiB
0
2423 
2
1049 
1
 
206

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2423
65.9%
2 1049
28.5%
1 206
 
5.6%

Length

2025-07-04T19:56:12.926389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-04T19:56:13.165761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 2423
65.9%
2 1049
28.5%
1 206
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 2423
65.9%
2 1049
28.5%
1 206
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2423
65.9%
2 1049
28.5%
1 206
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2423
65.9%
2 1049
28.5%
1 206
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2423
65.9%
2 1049
28.5%
1 206
 
5.6%

luxury_score
Real number (ℝ)

Zeros 

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.493475
Minimum0
Maximum174
Zeros463
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2025-07-04T19:56:13.462698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median59
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.06497
Coefficient of variation (CV)0.74223515
Kurtosis-0.88015164
Mean71.493475
Median Absolute Deviation (MAD)38
Skewness0.45920248
Sum262953
Variance2815.891
MonotonicityNot monotonic
2025-07-04T19:56:14.121622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 463
 
12.6%
49 348
 
9.5%
174 195
 
5.3%
44 60
 
1.6%
165 55
 
1.5%
38 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
37 45
 
1.2%
42 45
 
1.2%
Other values (151) 2313
62.9%
ValueCountFrequency (%)
0 463
12.6%
5 6
 
0.2%
6 6
 
0.2%
7 41
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 43
 
1.2%
ValueCountFrequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2025-07-04T19:55:55.373162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:41.035093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:43.163056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:45.021286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:47.062062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:49.331204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:51.020132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:52.578192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:53.805812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:55.502883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:41.237825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:43.336432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:45.229260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:47.329710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:49.511852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:51.199789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:52.727427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:53.931142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:55.631038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:41.518158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:43.541508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:45.476035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:47.570307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:49.698005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:51.405348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:52.847844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:54.072062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:55.766515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:41.814862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:43.763208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:45.706270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:47.840464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:49.891155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:51.605987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:52.999903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:54.393212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:55.909844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:42.078270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:44.000524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:45.967776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:48.176815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:50.086220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:51.842189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:53.142316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:54.533904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:56.035136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:42.279753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:44.210942image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:46.192939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:48.482844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:50.273185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:52.018382image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:53.283864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:54.654414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:56.172695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:42.562080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:44.441132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:46.412585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:48.748076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:50.428533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:52.184155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:53.398310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:54.805307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:56.313567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:42.798979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:44.635154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:46.638700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:48.934860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:50.623796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:52.318770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:53.538054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:54.998987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:56.482406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:42.974390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:44.836585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:46.849960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:49.133331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:50.819044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:52.444624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:53.655895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-07-04T19:55:55.214369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-07-04T19:56:14.522985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
agePossessionbalconybathroombedRoombuilt_up_areacarpet_areafacingfloorNumfurnishing_typeluxury_scoreotherspooja roompriceprice_per_sq_ftproperty_typeservant roomstore roomstudy roomsuper_built_up_area
agePossession1.0000.2740.1110.1300.0000.0000.0920.1250.2140.2560.1080.1870.1020.0560.3790.2870.1430.1410.086
balcony0.2741.0000.2250.1760.0000.0260.0160.0790.1770.2230.0820.1970.1360.0330.2140.4410.1460.1830.306
bathroom0.1110.2251.0000.862-0.0190.5990.044-0.0050.1980.1790.0700.2860.7200.4110.4720.5200.2440.1760.819
bedRoom0.1300.1760.8621.000-0.1350.5690.032-0.1040.1680.0570.0790.2910.6810.4170.5950.3170.2230.1540.800
built_up_area0.0000.000-0.019-0.1351.0000.9691.0000.3490.0880.2630.0000.000-0.001-0.4010.0000.0000.0000.0000.926
carpet_area0.0000.0260.5990.5690.9691.0000.0000.1590.0000.2390.0160.0000.6130.1360.0000.0000.0000.0030.894
facing0.0920.0160.0440.0321.0000.0001.0000.0000.0500.0650.0000.0290.0210.0000.0940.0360.0360.0000.000
floorNum0.1250.079-0.005-0.1040.3490.1590.0001.0000.0160.2320.0330.1020.001-0.1260.4850.0840.1120.0780.152
furnishing_type0.2140.1770.1980.1680.0880.0000.0500.0161.0000.2430.0610.2150.1750.0230.0820.2680.1540.1400.132
luxury_score0.2560.2230.1790.0570.2630.2390.0650.2320.2431.0000.1760.1890.2150.0540.3290.3470.2290.1830.222
others0.1080.0820.0700.0790.0000.0160.0000.0330.0610.1761.0000.0330.0340.0360.0260.0000.1060.0310.084
pooja room0.1870.1970.2860.2910.0000.0000.0290.1020.2150.1890.0331.0000.3340.0430.2520.2520.3050.3130.157
price0.1020.1360.7200.681-0.0010.6130.0210.0010.1750.2150.0340.3341.0000.7440.5430.3690.3030.2440.772
price_per_sq_ft0.0560.0330.4110.417-0.4010.1360.000-0.1260.0230.0540.0360.0430.7441.0000.2010.0440.0000.0300.287
property_type0.3790.2140.4720.5950.0000.0000.0940.4850.0820.3290.0260.2520.5430.2011.0000.0650.2410.1281.000
servant room0.2870.4410.5200.3170.0000.0000.0360.0840.2680.3470.0000.2520.3690.0440.0651.0000.1610.1850.584
store room0.1430.1460.2440.2230.0000.0000.0360.1120.1540.2290.1060.3050.3030.0000.2410.1611.0000.2260.046
study room0.1410.1830.1760.1540.0000.0030.0000.0780.1400.1830.0310.3130.2440.0300.1280.1850.2261.0000.121
super_built_up_area0.0860.3060.8190.8000.9260.8940.0000.1520.1320.2220.0840.1570.7720.2871.0000.5840.0460.1211.000

Missing values

2025-07-04T19:55:56.811865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-04T19:55:57.476878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-04T19:55:57.988088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sq_ftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatdlf the belairesector 5410.0024557.04072.158651301055Super Built up area 4072(378.3 sq.m.)Built Up area: 3000 sq.ft. (278.71 sq.m.)Carpet area: 2800 sq.ft. (260.13 sq.m.)4.05.03+17.0NorthModerately Old4072.03000.02800.0010001167
1flatimperia the esferasector 37c0.955703.01665.7899351218657Built Up area: 1578 (146.6 sq.m.)2.02.0017.0NaNUnder ConstructionNaN1578.0NaN01000066
2houseindependentsector 30.606000.0(93 sq.m.) Built-up AreaBuilt Up area: 1000 (92.9 sq.m.)1.01.001.0NaNUndefinedNaN1000.0NaN0000000
3houseu block dlf phase 3 road no 21sector 241.6029630.0(50 sq.m.) Plot AreaPlot area 60(50.17 sq.m.)7.05.03+4.0NaNOld PropertyNaN60.0NaN0000000
4flatmrg skylinesector 1061.8213392.01359.020310633214Built Up area: 1359 (126.26 sq.m.)Carpet area: 952 sq.ft. (88.44 sq.m.)3.02.024.0EastUndefinedNaN1359.0952.00000000
5flateldeco accoladesohna road0.724975.01447.2361809045226Super Built up area 1457(135.36 sq.m.)Carpet area: 849 sq.ft. (78.87 sq.m.)2.02.03+12.0EastRelatively New1457.0NaN849.0100000152
6flatramsons kshitijsector 950.17544.03125.0000000000005Carpet area: 3212 (298.4 sq.m.)1.01.0112.0North-EastRelatively NewNaNNaN3212.000000056
7flatparsvnath green villesector 481.508787.01707.067258449983Super Built up area 1707(158.59 sq.m.)Built Up area: 1600 sq.ft. (148.64 sq.m.)Carpet area: 1400 sq.ft. (130.06 sq.m.)3.03.031.0NorthOld Property1707.01600.01400.0000002128
8flatsilverglades the meliasohna road0.785777.01350.1817552362818Super Built up area 1350(125.42 sq.m.)2.02.039.0NaNUnder Construction1350.0NaNNaN10000072
9flatss the leafsector 851.777350.02408.1632653061224Super Built up area 2408(223.71 sq.m.)Built Up area: 1450 sq.ft. (134.71 sq.m.)Carpet area: 1300 sq.ft. (120.77 sq.m.)3.04.036.0South-EastModerately Old2408.01450.01300.0010002135
property_typesocietysectorpriceprice_per_sq_ftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3794flatm3m urbanasector 671.0012106.0826.0366760284156Built Up area: 826 (76.74 sq.m.)1.01.0117.0SouthRelatively NewNaN826.0NaN00000049
3795flatm3m capitalsector 1132.8712489.02298.022259588438Carpet area: 2298 (213.49 sq.m.)4.04.03+3.0North-EastUnder ConstructionNaNNaN2298.0001000065
3796flatconscient elevatesector 594.2518518.02295.0642617993303Super Built up area 2295(213.21 sq.m.)3.04.033.0NaNUnder Construction2295.0NaNNaN0000000
3797flatsignature global parksohna road0.906254.01439.078989446754Carpet area: 1439 (133.69 sq.m.)3.03.033.0North-WestRelatively NewNaNNaN1439.0000010096
3798houseindependentsector 438.4931444.0(251 sq.m.) Plot AreaPlot area 300(250.84 sq.m.)3.03.024.0NorthRelatively NewNaN300.0NaN01010221
3799flatmapsko mount villesector 791.508264.01815.1016456921589Super Built up area 1815(168.62 sq.m.)Carpet area: 1071.33 sq.ft. (99.53 sq.m.)3.04.03+11.0SouthRelatively New1815.0NaN1071.33010002144
3800houseindependentsector 269.0033333.0(251 sq.m.) Plot AreaPlot area 300(250.84 sq.m.)5.05.023.0South-WestOld PropertyNaN300.0NaN111101110
3801houseraj villassector 528.0025543.0(291 sq.m.) Carpet AreaCarpet area: 348 (290.97 sq.m.)6.05.03+4.0EastUndefinedNaNNaN348.000000000
3802flathsiidc sidco shivalik apartmentsmanesar0.855102.01666.0133281066248Super Built up area 1666(154.78 sq.m.)3.02.025.0NaNModerately Old1666.0NaNNaN0000000
3803NaNNaNsector 90NaNNaNNaNNaNNaNNaNNaNNaNNaNUndefinedNaNNaNNaN0000000